

from __future__ import annotations

lowercase			:				int   =     []






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							for i in range(len(lowerCamelCase_		)		):
														if board[row][i] == 1:
																					return False
							for i in range(len(lowerCamelCase_		)		):
														if board[i][column] == 1:
																					return False
							for i, j in zip(range(lowerCamelCase_     ,				-1     ,				-1		)     ,				range(lowerCamelCase_     ,				-1     ,				-1		)		):
														if board[i][j] == 1:
																					return False
							for i, j in zip(range(lowerCamelCase_     ,				-1     ,				-1		)     ,				range(lowerCamelCase_     ,				len(lowerCamelCase_		)		)		):
														if board[i][j] == 1:
																					return False
							return True






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							if row >= len(lowerCamelCase_		):
														solution.append(lowerCamelCase_		)
														printboard(lowerCamelCase_		)
														print()
														return True
							for i in range(len(lowerCamelCase_		)		):
														if is_safe(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
																					A      :					Tuple      		=					1
																					solve(lowerCamelCase_     ,				row + 1		)
																					A      :					Dict      		=					0
							return False






def        snake_case__		(    lowerCamelCase_		):
							for i in range(len(lowerCamelCase_		)		):
														for j in range(len(lowerCamelCase_		)		):
																					if board[i][j] == 1:
																												print('''Q'''     ,				end=''' '''		)
																					else:
																												print('''.'''     ,				end=''' '''		)
														print()


# n=int(input("The no. of queens"))
lowercase			:				Tuple   =     8
lowercase			:				Tuple   =     [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("The total no. of solutions are :", len(solution))









import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


lowercase			:				Dict   =     datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
		import pyspark







@dataclass
class 					__lowercase  (						datasets.BuilderConfig				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[datasets.Features]								=	None






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				):
							import pyspark

							def generate_fn():
														A      :					Dict      		=					df.select('''*'''     ,				pyspark.sql.functions.spark_partition_id().alias('''part_id'''		)		)
														for partition_id in partition_order:
																					A      :					Optional[Any]      		=					df_with_partition_id.select('''*'''		).where(F'part_id = {partition_id}'		).drop('''part_id'''		)
																					A      :					Dict      		=					partition_df.collect()
																					A      :					List[str]      		=					0
																					for row in rows:
																												yield F'{partition_id}_{row_id}', row.asDict()
																												row_id += 1

							return generate_fn







class 					__lowercase  (						_BaseExamplesIterable				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None    ,						)		->       List[str]:
														A      :					List[Any]      		=					df
														A      :					List[str]      		=					partition_order or range(self.df.rdd.getNumPartitions()						)
														A      :					Optional[int]      		=					_generate_iterable_examples(self.df    ,						self.partition_order						)




							def __iter__(					self						)		->       str:
														yield from self.generate_examples_fn()




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       "SparkExamplesIterable":
														A      :					Tuple      		=					list(range(self.df.rdd.getNumPartitions()						)						)
														generator.shuffle(__UpperCAmelCase						)
														return SparkExamplesIterable(self.df    ,						partition_order=__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       "SparkExamplesIterable":
														A      :					Tuple      		=					self.split_shard_indices_by_worker(__UpperCAmelCase    ,						__UpperCAmelCase						)
														return SparkExamplesIterable(self.df    ,						partition_order=__UpperCAmelCase						)






							@property
							def       snake_case			(					self						)		->       int:
														return len(self.partition_order						)







class 					__lowercase  (						datasets.DatasetBuilder				):





							"""simple docstring"""

							UpperCAmelCase_						:		List[str]								=	SparkConfig




							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       Optional[Any]:
														import pyspark

														A      :					Tuple      		=					pyspark.sql.SparkSession.builder.getOrCreate()
														A      :					List[Any]      		=					df
														A      :					List[Any]      		=					working_dir

														super().__init__(
														    cache_dir=__UpperCAmelCase    ,						config_name=str(self.df.semanticHash()						)    ,						**__UpperCAmelCase    ,						)




							def       snake_case			(					self						)		->       Optional[Any]:
														# Returns the path of the created file.
														def create_cache_and_write_probe(__UpperCAmelCase						):
																					# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
																					# already exist.
																					os.makedirs(self._cache_dir    ,						exist_ok=__UpperCAmelCase						)
																					A      :					Optional[Any]      		=					os.path.join(self._cache_dir    ,						'''fs_test''' + uuid.uuida().hex						)
																					# Opening the file in append mode will create a new file unless it already exists, in which case it will not
																					# change the file contents.
																					open(__UpperCAmelCase    ,						'''a'''						)
																					return [probe_file]

														if self._spark.conf.get('''spark.master'''    ,						''''''						).startswith('''local'''						):
																					return

														# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
														# accessible to the driver.
														# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
														if self._cache_dir:
																					A      :					Optional[int]      		=					(
																					    self._spark.sparkContext.parallelize(range(1						)    ,						1						).mapPartitions(__UpperCAmelCase						).collect()
																					)
																					if os.path.isfile(probe[0]						):
																												return

														raise ValueError(
														    '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'''						)




							def       snake_case			(					self						)		->       List[Any]:
														return datasets.DatasetInfo(features=self.config.features						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       str:
														return [datasets.SplitGenerator(name=datasets.Split.TRAIN						)]




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       List[Any]:
														import pyspark

														def get_arrow_batch_size(__UpperCAmelCase						):
																					for batch in it:
																												yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]}						)

														A      :					Any      		=					self.df.count()
														A      :					Tuple      		=					df_num_rows if df_num_rows <= 1_00 else 1_00
														# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
														A      :					Dict      		=					(
														    self.df.limit(__UpperCAmelCase						)
														    .repartition(1						)
														    .mapInArrow(__UpperCAmelCase    ,						'''batch_bytes: long'''						)
														    .agg(pyspark.sql.functions.sum('''batch_bytes'''						).alias('''sample_bytes'''						)						)
														    .collect()[0]
														    .sample_bytes
														    / sample_num_rows
														)
														A      :					str      		=					approx_bytes_per_row * df_num_rows
														if approx_total_size > max_shard_size:
																					# Make sure there is at least one row per partition.
																					A      :					Union[str, Any]      		=					min(__UpperCAmelCase    ,						int(approx_total_size / max_shard_size						)						)
																					A      :					List[Any]      		=					self.df.repartition(__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						)		->       Iterable[Tuple[int, bool, Union[int, tuple]]]:
														import pyspark

														A      :					Dict      		=					ParquetWriter if file_format == '''parquet''' else ArrowWriter
														A      :					Optional[Any]      		=					os.path.join(self._working_dir    ,						os.path.basename(__UpperCAmelCase						)						) if self._working_dir else fpath
														A      :					int      		=					file_format == '''parquet'''

														# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
														# pickling the SparkContext.
														A      :					Any      		=					self.config.features
														A      :					Any      		=					self._writer_batch_size
														A      :					int      		=					self._fs.storage_options

														def write_arrow(__UpperCAmelCase						):
																					# Within the same SparkContext, no two task attempts will share the same attempt ID.
																					A      :					List[Any]      		=					pyspark.TaskContext().taskAttemptId()
																					A      :					Union[str, Any]      		=					next(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					if first_batch is None:
																												# Some partitions might not receive any data.
																												return pa.RecordBatch.from_arrays(
																												    [[task_id], [0], [0]]    ,						names=['''task_id''', '''num_examples''', '''num_bytes''']    ,						)
																					A      :					Optional[int]      		=					0
																					A      :					Optional[int]      		=					writer_class(
																					    features=__UpperCAmelCase    ,						path=working_fpath.replace('''SSSSS'''    ,						f'{shard_id:05d}'						).replace('''TTTTT'''    ,						f'{task_id:05d}'						)    ,						writer_batch_size=__UpperCAmelCase    ,						storage_options=__UpperCAmelCase    ,						embed_local_files=__UpperCAmelCase    ,						)
																					A      :					Tuple      		=					pa.Table.from_batches([first_batch]						)
																					writer.write_table(__UpperCAmelCase						)
																					for batch in it:
																												if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																																			A		,		A      :					str      		=					writer.finalize()
																																			writer.close()
																																			yield pa.RecordBatch.from_arrays(
																																			    [[task_id], [num_examples], [num_bytes]]    ,						names=['''task_id''', '''num_examples''', '''num_bytes''']    ,						)
																																			shard_id += 1
																																			A      :					List[str]      		=					writer_class(
																																			    features=writer._features    ,						path=working_fpath.replace('''SSSSS'''    ,						f'{shard_id:05d}'						).replace('''TTTTT'''    ,						f'{task_id:05d}'						)    ,						writer_batch_size=__UpperCAmelCase    ,						storage_options=__UpperCAmelCase    ,						embed_local_files=__UpperCAmelCase    ,						)
																												A      :					str      		=					pa.Table.from_batches([batch]						)
																												writer.write_table(__UpperCAmelCase						)

																					if writer._num_bytes > 0:
																												A		,		A      :					Tuple      		=					writer.finalize()
																												writer.close()
																												yield pa.RecordBatch.from_arrays(
																												    [[task_id], [num_examples], [num_bytes]]    ,						names=['''task_id''', '''num_examples''', '''num_bytes''']    ,						)

																					if working_fpath != fpath:
																												for file in os.listdir(os.path.dirname(__UpperCAmelCase						)						):
																																			A      :					Union[str, Any]      		=					os.path.join(os.path.dirname(__UpperCAmelCase						)    ,						os.path.basename(__UpperCAmelCase						)						)
																																			shutil.move(__UpperCAmelCase    ,						__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					(
														    self.df.mapInArrow(__UpperCAmelCase    ,						'''task_id: long, num_examples: long, num_bytes: long'''						)
														    .groupBy('''task_id'''						)
														    .agg(
														        pyspark.sql.functions.sum('''num_examples'''						).alias('''total_num_examples'''						)    ,						pyspark.sql.functions.sum('''num_bytes'''						).alias('''total_num_bytes'''						)    ,						pyspark.sql.functions.count('''num_bytes'''						).alias('''num_shards'''						)    ,						pyspark.sql.functions.collect_list('''num_examples'''						).alias('''shard_lengths'''						)    ,						)
														    .collect()
														)
														for row in stats:
																					yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = "arrow"    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       List[str]:
														self._validate_cache_dir()

														A      :					int      		=					convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE						)
														self._repartition_df_if_needed(__UpperCAmelCase						)
														A      :					Any      		=					not is_remote_filesystem(self._fs						)
														A      :					Union[str, Any]      		=					os.path.join if is_local else posixpath.join

														A      :					Any      		=					'''-TTTTT-SSSSS-of-NNNNN'''
														A      :					Any      		=					f'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
														A      :					List[Any]      		=					path_join(self._output_dir    ,						__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					0
														A      :					Any      		=					0
														A      :					Tuple      		=					0
														A      :					Union[str, Any]      		=					[]
														A      :					List[str]      		=					[]

														for task_id, content in self._prepare_split_single(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						):
																					(
																					    (
																					    A
																					)		,		(
																					    A
																					)		,		(
																					    A
																					)		,		(
																					    A
																					)		,		
																					)      :					List[Any]      		=					content
																					if num_bytes > 0:
																												total_num_examples += num_examples
																												total_num_bytes += num_bytes
																												total_shards += num_shards
																												task_id_and_num_shards.append((task_id, num_shards)						)
																												all_shard_lengths.extend(__UpperCAmelCase						)

														A      :					List[str]      		=					total_num_examples
														A      :					List[str]      		=					total_num_bytes

														# should rename everything at the end
														logger.debug(f'Renaming {total_shards} shards.'						)
														if total_shards > 1:
																					A      :					Tuple      		=					all_shard_lengths

																					# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
																					# pickling error due to pickling the SparkContext.
																					A      :					Union[str, Any]      		=					self._fs

																					# use the -SSSSS-of-NNNNN pattern
																					def _rename_shard(
																					    __UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						):
																												rename(
																												    __UpperCAmelCase    ,						fpath.replace('''SSSSS'''    ,						f'{shard_id:05d}'						).replace('''TTTTT'''    ,						f'{task_id:05d}'						)    ,						fpath.replace('''TTTTT-SSSSS'''    ,						f'{global_shard_id:05d}'						).replace('''NNNNN'''    ,						f'{total_shards:05d}'						)    ,						)

																					A      :					Tuple      		=					[]
																					A      :					Optional[int]      		=					0
																					for i in range(len(__UpperCAmelCase						)						):
																												A		,		A      :					Tuple      		=					task_id_and_num_shards[i]
																												for shard_id in range(__UpperCAmelCase						):
																																			args.append([task_id, shard_id, global_shard_id]						)
																																			global_shard_id += 1
																					self._spark.sparkContext.parallelize(__UpperCAmelCase    ,						len(__UpperCAmelCase						)						).map(lambda __UpperCAmelCase						: _rename_shard(*__UpperCAmelCase						)						).collect()
														else:
																					# don't use any pattern
																					A      :					int      		=					0
																					A      :					Tuple      		=					task_id_and_num_shards[0][0]
																					self._rename(
																					    fpath.replace('''SSSSS'''    ,						f'{shard_id:05d}'						).replace('''TTTTT'''    ,						f'{task_id:05d}'						)    ,						fpath.replace(__UpperCAmelCase    ,						''''''						)    ,						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						)		->       SparkExamplesIterable:
														return SparkExamplesIterable(self.df						)






